An Extension to Basis-Hypervectors for Learning from Circular Data in Hyperdimensional Computing

TL;DR

This paper enhances hyperdimensional computing by improving basis-hypervectors for real number encoding and introducing a method to learn from circular data, significantly boosting accuracy in classification and regression tasks.

Contribution

It proposes an improved encoding for real numbers and a novel learning method for circular data within hyperdimensional computing.

Findings

More accurate models for circular data classification.

Enhanced regression performance with circular data.

Effective encoding improvements for real number representation.

Abstract

Hyperdimensional Computing (HDC) is a computation framework based on properties of high-dimensional random spaces. It is particularly useful for machine learning in resource-constrained environments, such as embedded systems and IoT, as it achieves a good balance between accuracy, efficiency and robustness. The mapping of information to the hyperspace, named encoding, is the most important stage in HDC. At its heart are basis-hypervectors, responsible for representing the smallest units of meaningful information. In this work we present a detailed study on basis-hypervector sets, which leads to practical contributions to HDC in general: 1) we propose an improvement for level-hypervectors, used to encode real numbers; 2) we introduce a method to learn from circular data, an important type of information never before addressed in machine learning with HDC. Empirical results indicate that these contributions lead to considerably more accurate models for both classification and regression with circular data.